In order to obtain fixed-size representation vectors from utterances of variable duration and arbitrary word sequences (i.e. text-independent), a pooling method is required. The most common pooling method is a simple aggregation over the time axis, where the mean vector is concatenated with standard deviation (known as statistical pooling layer). The speaker embedding (a vector of few hundred dimensions) is obtained by projecting this pooled representation (a vector of few thousand dimensions) onto a linear layer or a shallow feed-forward neural network. Other pooling approaches have been proposed, such as single and multi-head attention, as well as the use of higher-order statistics [1]}, [2]}, [3]}.
